This is an unofficial PyTorch implementation of FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. The official Tensorflow implementation is here.
This code is only available in FixMatch (RandAugment).
| #Labels | 40 | 250 | 4000 |
|---|---|---|---|
| Paper (RA) | 86.19 ± 3.37 | 94.93 ± 0.65 | 95.74 ± 0.05 |
| This code | 93.60 | 95.31 | 95.77 |
| Acc. curve | link | link | link |
* November 2020. Retested after fixing EMA issues.
| #Labels | 400 | 2500 | 10000 |
|---|---|---|---|
| Paper (RA) | 51.15 ± 1.75 | 71.71 ± 0.11 | 77.40 ± 0.12 |
| This code | 57.50 | 72.93 | 78.12 |
| Acc. curve | link | link | link |
* Training using the following options --amp --opt_level O2 --wdecay 0.001
Train the model by 4000 labeled data of CIFAR-10 dataset:
python train.py --dataset cifar10 --num-labeled 4000 --arch wideresnet --batch-size 64 --lr 0.03 --expand-labels --seed 5 --out results/[email protected]
Train the model by 10000 labeled data of CIFAR-100 dataset by using DistributedDataParallel:
python -m torch.distributed.launch --nproc_per_node 4 ./train.py --dataset cifar100 --num-labeled 10000 --arch wideresnet --batch-size 16 --lr 0.03 --wdecay 0.001 --expand-labels --seed 5 --out results/cifar100@10000
tensorboard --logdir=<your out_dir>
- python 3.6+
- torch 1.4
- torchvision 0.5
- tensorboard
- numpy
- tqdm
- apex (optional)
- Official TensorFlow implementation of FixMatch
- Unofficial PyTorch implementation of MixMatch
- Unofficial PyTorch Reimplementation of RandAugment
- PyTorch image models
@misc{jd2020fixmatch,
author = {Jungdae Kim},
title = {PyTorch implementation of FixMatch},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/kekmodel/FixMatch-pytorch}}
}